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A total of 19 patients in whom only an incomplete data set with more than two missing variables was obtained were excluded from further analysis.
Although the amount of missing variables was very small, in order to include all of the collected information, the missing data modelling procedure implemented in the Mplus programme was used.
Any subject with one or more missing variables was removed from the study, reducing the sample size from 1 324 to 1 234.
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According to the low missing frequency, missing variables were imputed by its highest frequency value [ 28].
(4) The maximum likelihood estimation of the missing variables is obtained after convergence. .
For these variables, the case fatality rate was the same or lower in patients with missing data compared to patients with documented results, supporting our assumption that missing variables were likely within the normal range [data not shown].
Cases with missing variables were excluded from the analysis.
In case of missing data, missing variables were excluded from both numerator and denominator.
Subjects with missing variables were excluded from analyses involving the specific variable.
Irregularly missing variables were imputed under the assumption of missingness at random [ 34, 35].
Records where all physiological variables were missing were excluded and for the remaining records, missing variables were replaced to the normal range and weighted accordingly [ 28].
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